from directed_graphs.datasets import directed_swiss_roll_sklearn, plot_directed_3d
X, flow, labels = directed_swiss_roll_sklearn(num_nodes=2000)

X = torch.tensor(X).float().to(device)
flow = torch.tensor(flow).float().to(device)
X = X.float().to(device)
flow = flow.float().to(device)

BOBO_FET = DiffusionDistanceFlowEmbedder(X, flow, labels = labels, device = device)

/gpfs/loomis/project/krishnaswamy_smita/kjm76/directed_graphs/directed_graphs/utils.py:12: UserWarning: Sparsifying input to bsr_array
  warnings.warn("Sparsifying input to bsr_array")

using sigma =  2.298348
[1.0000014 +0.j 0.9774393 +0.j 0.9697032 +0.j 0.954148  +0.j
 0.94269323+0.j]

/gpfs/loomis/project/krishnaswamy_smita/kjm76/conda_envs/flowembed/lib/python3.9/site-packages/matplotlib/transforms.py:775: ComplexWarning: Casting complex values to real discards the imaginary part
  points = np.asarray(points, float)

BOBO_FET.fit()

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BOBO_FET.visualize_embedding()

BOBO_FET.training_gif(duration=400)

BOBO_FET.visualize_loss()

BOBO_FET.losses['diffusion']

100

import glob
import ipywidgets as widgets
from PIL import Image
import os
import ipywidgets as widgets
import base64
frames = [Image.open(image) for image in glob.glob(f"visualizations/{BOBO_FET.timestamp}/*.jpg")]
frame_one = frames[0]
frame_one.save(f"{BOBO_FET.title}.gif", format="GIF", append_images=frames,
           save_all=True, duration=300, loop=0)
# display in jupyter notebook
b64 = base64.b64encode(open(f"{BOBO_FET.title}.gif",'rb').read()).decode('ascii')
display(widgets.HTML(f'<img src="data:image/gif;base64,{b64}" />'))

class DiffusionDistanceFlowEmbedder[source]

`class` `DiffusionDistanceFlowEmbedder`[source]